ColOpenData can be used to access open geospatial data from Colombia. This data is retrieved from the Geostatistical National Framework (MGN), published by the National Administrative Department of Statistics (DANE). The MGN contains the political-administrative division and is used to reference census statistical information. Further information can be obtained directly from DANE here.
This package contains the 2018’s version of the MGN, which also
included a summarized version of the National Population and Dwelling
Census (CNPV) in different aggregation levels. Each level is stored in a
different dataset, which can be retrieved using the
download_geospatial function, which requires three
arguments:
-
datasetcharacter with the geospatial dataset name. -
include_geomlogical for including (or not) geometry. Default isTRUE. -
include_cnpvlogical for including (or not) CNPV demographic and socioeconomic information. Default isTRUE.
To better understand dataset names and details please refer to Documentation and Dictionaries.
Details for geospatial datasets relate to the level of aggregation as follows:
| Code | Level |
|---|---|
| DANE_MGN_2018_DPTO | Department |
| DANE_MGN_2018_MPIO | Municipality |
| DANE_MGN_2018_MPIOCL | Municipality including Class |
| DANE_MGN_2018_MZN | Block |
| DANE_MGN_2018_SECR | Rural Sector |
| DANE_MGN_2018_SECU | Urban Sector |
| DANE_MGN_2018_SETR | Rural Section |
| DANE_MGN_2018_SETU | Urban Section |
| DANE_MGN_2018_ZU | Urban Zone |
In this vignette you will learn:
- How to download geospatial data using ColOpenData
- How to use census data included in geospatial datasets
- How to visualize spatial data using leaflet and ggplot2
We will be using geospatial data at the level of Municipality (MPIO) for the department of Tolima and we will calculate the percentage of houses with internet connection at each municipality. Later, we will build some plots using the previously mentioned approaches for dynamic and static plots.
We will start by importing the needed libraries.
Disclaimer: all data is loaded to the environment in the user’s R session, but is not downloaded to user’s computer. Spatial datasets can be very long and might take a while to be loaded in the environment
Downloading geospatial data
First, we download the data using the function
download_geospatial, including the geometries and the
census related information.
mpio <- download_geospatial(
dataset = "DANE_MGN_2018_MPIO",
include_geom = TRUE,
include_cnpv = TRUE
)
#> Original data is retrieved from the National Administrative Department
#> of Statistics (Departamento Administrativo Nacional de Estadística -
#> DANE).
#> Reformatted by package authors.
#> Stored by Universidad de Los Andes under the Epiverse TRACE iniative.
head(mpio)
#> Simple feature collection with 6 features and 90 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -76.1027 ymin: 0.9764735 xmax: -74.89527 ymax: 2.326755
#> Geodetic CRS: WGS 84
#> DPTO_CCDGO MPIO_CCDGO MPIO_CNMBR MPIO_CDPMP VERSION AREA
#> 1 18 001 FLORENCIA 18001 2018 2547637532
#> 2 18 029 ALBANIA 18029 2018 414122070
#> 3 18 094 BELÉN DE LOS ANDAQUÍES 18094 2018 1191618572
#> 4 18 247 EL DONCELLO 18247 2018 1106076151
#> 5 18 256 EL PAUJÍL 18256 2018 1234734145
#> 6 18 410 LA MONTAÑITA 18410 2018 1701061430
#> LATITUD LONGITUD STCTNENCUE STP3_1_SI STP3_2_NO STP3A_RI STP3B_TCN
#> 1 1.749139 -75.55824 71877 32 71845 32 0
#> 2 1.227865 -75.88233 2825 24 2801 24 0
#> 3 1.500923 -75.87565 4243 54 4189 54 0
#> 4 1.791386 -75.19394 8809 0 8809 0 0
#> 5 1.617746 -75.23404 5795 0 5795 0 0
#> 6 1.302860 -75.23573 5113 15 5098 15 0
#> STP4_1_SI STP4_2_NO STP9_1_USO STP9_2_USO STP9_3_USO STP9_4_USO STP9_2_1_M
#> 1 0 71877 61176 2178 8436 87 39
#> 2 0 2825 1826 49 948 2 3
#> 3 1 4242 3223 109 900 11 4
#> 4 0 8809 6598 357 1850 4 11
#> 5 0 5795 4891 204 695 5 4
#> 6 0 5113 4077 241 786 9 2
#> STP9_2_2_M STP9_2_3_M STP9_2_4_M STP9_2_9_M STP9_3_1_N STP9_3_2_N STP9_3_3_N
#> 1 1550 566 18 5 54 2591 1061
#> 2 34 12 0 0 3 21 32
#> 3 99 6 0 0 8 88 6
#> 4 259 87 0 0 21 334 124
#> 5 161 38 1 0 5 239 104
#> 6 205 32 2 0 3 103 123
#> STP9_3_4_N STP9_3_5_N STP9_3_6_N STP9_3_7_N STP9_3_8_N STP9_3_9_N STP9_3_10
#> 1 535 368 3172 233 7 19 371
#> 2 728 53 92 5 0 0 14
#> 3 2 61 626 8 0 8 93
#> 4 807 89 361 42 0 27 39
#> 5 4 70 211 16 0 0 45
#> 6 17 84 362 30 0 0 58
#> STP9_3_99 STVIVIENDA STP14_1_TI STP14_2_TI STP14_3_TI STP14_4_TI STP14_5_TI
#> 1 25 63354 47817 13764 1624 21 8
#> 2 0 1875 1793 40 22 17 0
#> 3 0 3332 3189 113 24 2 2
#> 4 6 6955 6006 775 160 1 1
#> 5 1 5095 4700 145 188 4 1
#> 6 6 4318 3890 224 161 6 2
#> STP14_6_TI STP15_1_OC STP15_2_OC STP15_3_OC STP15_4_OC TSP16_HOG STP19_EC_1
#> 1 120 49809 2681 2150 8714 51430 48638
#> 2 3 1409 13 55 398 1559 1300
#> 3 2 2883 2 107 340 3161 2595
#> 4 12 5767 304 388 496 6129 5375
#> 5 57 4568 5 323 199 5848 4195
#> 6 35 3553 151 308 306 3748 2159
#> STP19_ES_2 STP19_EE_1 STP19_EE_2 STP19_EE_3 STP19_EE_4 STP19_EE_5 STP19_EE_6
#> 1 1171 34851 10343 2169 509 13 3
#> 2 109 1184 106 1 0 0 0
#> 3 288 2118 366 17 2 1 0
#> 4 392 3548 962 793 1 1 1
#> 5 373 3330 770 84 0 1 1
#> 6 1394 1964 144 9 1 0 0
#> STP19_EE_9 STP19_ACU1 STP19_ACU2 STP19_ALC1 STP19_ALC2 STP19_GAS1 STP19_GAS2
#> 1 750 45179 4630 41138 8671 37028 12074
#> 2 9 808 601 703 706 26 1371
#> 3 91 2017 866 1806 1077 52 2796
#> 4 69 4175 1592 4323 1444 57 5549
#> 5 9 2505 2063 2359 2209 1463 3041
#> 6 41 1441 2112 1329 2224 67 3454
#> STP19_GAS9 STP19_REC1 STP19_REC2 STP19_INT1 STP19_INT2 STP19_INT9 STP27_PERS
#> 1 707 45491 4318 13362 35727 720 156789
#> 2 12 727 682 27 1370 12 4514
#> 3 35 1905 978 73 2775 35 9075
#> 4 161 4348 1419 211 5395 161 17775
#> 5 64 2414 2154 125 4379 64 13014
#> 6 32 1273 2280 64 3457 32 12128
#> STPERSON_L STPERSON_S STP32_1_SE STP32_2_SE STP34_1_ED STP34_2_ED STP34_3_ED
#> 1 4315 152474 77620 79169 25503 30249 29951
#> 2 151 4363 2323 2191 725 1016 717
#> 3 346 8729 4551 4524 1592 2254 1388
#> 4 203 17572 8790 8985 3047 3811 2601
#> 5 192 12822 6601 6413 2346 2882 2170
#> 6 604 11524 6437 5691 2229 3022 1836
#> STP34_4_ED STP34_5_ED STP34_6_ED STP34_7_ED STP34_8_ED STP34_9_ED STP51_PRIM
#> 1 23602 17235 14349 8969 4687 2244 37918
#> 2 568 536 445 253 162 92 1696
#> 3 1121 986 816 487 286 145 2596
#> 4 2302 2032 1792 1135 707 348 6091
#> 5 1587 1460 1188 703 430 248 4805
#> 6 1563 1441 1010 578 323 126 5011
#> STP51_SECU STP51_SUPE STP51_POST STP51_13_E STP51_99_E Shape_Leng Shape_Area
#> 1 14123 14606 856 5892 3799 2.942508 0.20692777
#> 2 150 98 0 215 46 1.112829 0.03361758
#> 3 418 171 12 720 123 2.234657 0.09674460
#> 4 712 347 26 1095 171 3.154370 0.08986744
#> 5 261 226 0 916 99 3.529316 0.10030928
#> 6 384 134 0 724 182 3.402939 0.13817351
#> geom
#> 1 MULTIPOLYGON (((-75.42074 2...
#> 2 MULTIPOLYGON (((-75.89506 1...
#> 3 MULTIPOLYGON (((-75.78705 1...
#> 4 MULTIPOLYGON (((-75.36167 2...
#> 5 MULTIPOLYGON (((-75.36638 2...
#> 6 MULTIPOLYGON (((-75.40346 1...After downloading, we have to filter by the municipality code using the DIVIPOLA code for Tolima. For further details on DIVIPOLA codification and functions please refer to Documentation and Dictionaries
divipola_department_code("TOLIMA")
#> [1] "73"To understand which column contains the departments’ codes and filter
for Tolima, we will need the corresponding dataset dictionary. To
download the dictionary we can use the dictionary function.
This function uses the dataset name to download the associated
information. For further information please refer to the documentation
on dictionaries previously mentioned.
dict <- dictionary("DANE_MGN_2018_MPIO")
head(dict)
#> variable tipo longitud
#> 1 DPTO_CCDGO Text 2
#> 2 MPIO_CCDGO Text 3
#> 3 MPIO_CNMBR Text 250
#> 4 MPIO_CDPMP Text 5
#> 5 VERSION Long Integer NA
#> 6 AREA Double NA
#> descripcion
#> 1 Código del departamento
#> 2 Código que identifica al municipio
#> 3 Nombre del municipio
#> 4 Código concatenado que identifica al municipio
#> 5 Año de la información geográfica
#> 6 Área del municipio en metros cuadrados (Sistema de coordenadas planas MAGNA_Colombia_Bogota)
#> categoria_original
#> 1 <NA>
#> 2 <NA>
#> 3 <NA>
#> 4 <NA>
#> 5 <NA>
#> 6 <NA>After exploring the dictionary, we can identify the column that contains the individual municipality codes is DPTO_CCDGO. We will filter based on that column.
To calculate the percentage of houses with internet connection, we will need to know the number of houses with internet connection and the total of houses in each SECU. From the dictionary we get that the number of houses with internet connection is STP19_INT1 and the total of houses is STVIVIENDA. We will calculate the percentage as follows:
Static plots (ggplot2)
ggplot2 can
be used to generate static plots of spatial data by using the geometry
geom_sf as follows:
The generated plot by default uses a blue palette, which makes it
hard to observe small differences in internet coverage across
municipalities. Color palettes and themes can be defined for each plot
using the aesthetic and scales, which can be consulted in the
ggplot2 documentation.
We will use a gradient with a two-color diverging palette, to make the
differences more visible.
ggplot(data = tolima) +
geom_sf(mapping = aes(fill = INT_PERC), color = NA) +
theme_minimal() +
theme(
panel.grid = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()
) +
scale_fill_gradient("Percentage", low = "#10bed2", high = "#deff00") +
ggtitle(
label = "Internet coverage",
subtitle = "Tolima, Colombia"
)
Dynamic plots (leaflet)
For dynamic plots, we can use leaflet,
which is an open-source library for interactive maps. To create the same
plot we first will create the color palette.
colfunc <- colorRampPalette(c("#10bed2", "#deff00"))
pal <- colorNumeric(
palette = colfunc(100),
domain = tolima$INT_PERC
)With the previous color palette we can generate the interactive plot.
The package also includes open source maps for the base map like OpenStreetMap
and CartoDB. For further
details on leaflet, please refer to the package’s documentation.
leaflet(tolima) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addPolygons(
stroke = TRUE,
weight = 0,
color = NA,
fillColor = ~ pal(tolima$INT_PERC),
fillOpacity = 1,
popup = paste0(tolima$INT_PERC)
) %>%
addLegend(
position = "bottomright",
pal = pal,
values = ~ tolima$INT_PERC,
opacity = 1,
title = "Internet Coverage"
)